One of the bottlenecks in human memory capacity is its “filtering efficiency” – irrelevant information in memory only detracts from an already-constrained memory span. New work by McNab & Klingberg images the neural structure directly responsible for such filtering, and shows it can predict behavioral measures of memory span. Impressively, the location of this “memory filter” is the globus pallidus, as predicted by a computational network model of cortex, but in contrast to that model, it shows functional correlations with parietal in addition to frontal areas. This work has immediate implications for understanding how the brain accomplishes attention and goal-directed behavior, including topics like perceptual load, the attentional blink, and methods for enhancing memory.
Each of 25 subjects completed 120 trials of a simple task inside an fMRI scanner: they were to remember the locations of circles and later indicate whether a circle had been present at a particular location. Critically, some of these trials contained 5 circles to be remembered (the “5-no distractor” trials), others contained only 3 circles to be remembered (the “3-no distractor trials”), and yet others contained both 3 circles to be remembered and 2 circles to be ignored (based on their color – the “5-distractor trials”). Each subject was informed whether the upcoming trial would be a distractor or no-distractor trial in an instruction phase preceding each trial.

The activity of the “cortical filter” was revealed in increased neural activity during the instruction phase in distractor trials. Furthermore, each subject’s individual memory capacity was predictive of the extent to which they activated this filter (reflected in increased middle frontal gyrus/DLPFC and left basal ganglia/globus pallidus), consistent with previous evidence that the regions involved constitute a “fronto-striatal” network for attention & working memory.

The efficacy of this “cortical filter” – i.e., the remembrance of the to-be-ignored yellow circles – was reflected in more similar neural activity between the distractor-5 trials and the no-distractor-5 trials. Conversely, more similar neural activity between distractor-5 and no-distractor-3 trials indicates the effective filtering of those to-be-ignored yellow circles. Thus the authors calculated an “unnecessary storage activity” index on this basis using neural activity in the posterior parietal cortex, a region of the brain involved in the short-term maintenance of structured data (especially from the visual modality).

This neural index of filtering was predictive only of activity in the basal ganglia/globus pallidus – not the activity of more frontal regions. This indicates that filtering takes place in the globus pallidus. The index was also related to each subject’s memory span, as well as each subject’s loss of accuracy in the distractor-5 condition relative to the distractor-3 condition, revealing a direct role for this region in the functional efficacy of memory.

This advance also provides new insight into Nillie Lavie’s work, who has shown that under conditions of high memory load, memory filtering becomes less effective. According to computational models, basal ganglia are involved not only in filtering information from the external world, but also in keeping important information inside memory. Thus there appears to be a tradeoff between using basal ganglia to maintain information vs using it to filter incoming information. More interestingly, Lavie’s demonstrations that filtering is improved by high “perceptual load” may reflect the advantage conferred by keeping globus pallidus in an engaged state (equivalent to the increased activity for distractor vs no-distractor trials demonstrated above by McNab & Klingberg).

A more perceptual aspect to cortical filtering can be seen in the attentional blink paradigm, a phenomenon where subjects consciously see only the first of two rapidly presented images. This phenomenon has been interpreted to reflect a relatively stable individual difference – there appear to be “blinkers” and “nonblinkers” – but the origin of this difference is unknown (it does not appear to be related to the p3 response). HOWEVER, what looks to be an incredible paper shows the attentional blink can be affected by meditation. Interestingly, meditation is a perfect example of an exercise in the filtering of distractors, and may thereby improve the dynamics of filtering in globus pallidus to allow for rapid attentional selection of multiple targets in the attentional blink (cool!).

Comments

Hello Chris, great post. Yes, meditation is a form of “mental training” with growing research behind, and has multiple benefits, but don’t forget that Klingberg himself has developed a computer-based program to train working memory, with equally impressive results, in a variety of contexts (kids with ADD/ ADHD, normal college students, stroke rehab for adult). Fascinating times.

Chris, could you explain the seeming contradiction of the research that shows distraction *reduces* the attentional blink and mediation, apparently by doing the opposite, cutting down on distraction, also reduces the attention blink?